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This study used a field-programmable gate array (FPGA) with a Xilinx Spartan-3 FPGA to implement Reflex charge control in a dual-axis solar tracking system with maximum power point tracking (MPPT). The chaos embedded particle swarm optimization method was used to search for the optimum gain constants of the PI controller and the Reflex charging frequency. This scheme not only increases the output power of solar panels but also has a significant effect on switching loss and oscillation of solar charging. The experiment results showed that the proposed method can also significantly improve temperature rise, and that charging efficiency is also better than it is in a traditional charge mode. The results also showed that charging power was enough for solar tracking and the requirements of the charging system. The most significant contribution of this paper is that the scheme can be applied to any active solar tracking and charging system.

This system was composed of independently developed ECG Signal Acquisition Model, DAQ Signal Acquisition Card, and the LabVIEW Graphics System. Regarding the ECG Signal Acquisition Model, this system uses the TL082 Operational Amplifier to realize the amplification, and uses the Filter Circuits along with the ISO122 to isolate IC. Regarding the ECG oscillogram display and ECG Signal Acquisition, the LabVIEW Graphics System developed by the American company “National Instruments” (NI) was adopted. Also, the LabVIEW system and the DAQ signal acquisition card of the Data Acquisition Package developed by the NI Company was adopted as well.The DAQ signal acquisition card can engage in A/D, DI/O, number counting and time counting applications. At last, it can use the TCP/IP Protocol to send the ECG waveform data by the internet to the computers with remote surveillance monitors. The remote surveillance computers can be installed in the hospital. Professional doctors in the hospital can observe the patient’s ECG waveform data and statistics, and engage in the job of remote care.

This This paper proposed method is to use a Smart phone in the real - time situation to carry out monitoring and controlling factory zone temperature, humidity, air quality, flame detection, staff not near the danger zone detection and electrical load analysis. The monitoring range also includes operating machine vibration detection in the factory area, so these methods are innovative research. Our research proposes the integration of ZigBee and Wi-Fi protocol intelligent monitoring system within the framework of the entire plant. The factory sensor using the ZigBee protocol to deliver the message, and real-time sensing data is sent to our integrated embedded systems. Our paper presents an integrated embedded systems using the open-source Arduino DUE module, which is a 32-bit ARM core. Our study proposed a way that writes the network code to ARM chipset become integrated controller. The intelligent integrated controller will instantly analytical processing by the ZigBee sensor pass to the message. Simultaneously this study using Android APP and web-based methods to show the measurement results. The web-based and Android APP approaches will transfer these results to specify the cloud device by way of the TCP / IP protocol. Finally, this paper utilizes FFT (Fast Fourier Transform) approach to analysis of power loads in the factory zones. Simultaneously this study also uses NFC (Near Field Communication) technology to carry out the actual measurement of the total electricity load experiments by smart phones.